Automatic epileptic seizure onset detection using matching pursuit: a case study

TY - JOUR

T1 - Automatic epileptic seizure onset detection using matching pursuit: a case study

AU - Sorensen, Thomas L

AU - Olsen, Ulrich L

AU - Conradsen, Isa

AU - Duun-Henriksen, Jonas

AU - Kjaer, Troels W

AU - Thomsen, Carsten E

AU - Sorensen, Helge B D

PY - 2010/8/31

Y1 - 2010/8/31

N2 - An automatic alarm system for detecting epileptic seizure onsets could be of great assistance to patients and medical staff. A novel approach is proposed using the Matching Pursuit algorithm as a feature extractor combined with the Support Vector Machine (SVM) as a classifier for this purpose. The combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before, making this a pilot study. Data from red different patients with 6 to 49 seizures are used to test our model. Three patients are recorded with scalp electroencephalography (sEEG) and three with intracranial electroencephalography (iEEG). A sensitivity of 78-100% and a detection latency of 5-18s has been achieved, while holding the false detection at 0.16-5.31/h. Our results show the potential of Matching Pursuit as a feature extractor for detection of epileptic seizures.

AB - An automatic alarm system for detecting epileptic seizure onsets could be of great assistance to patients and medical staff. A novel approach is proposed using the Matching Pursuit algorithm as a feature extractor combined with the Support Vector Machine (SVM) as a classifier for this purpose. The combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before, making this a pilot study. Data from red different patients with 6 to 49 seizures are used to test our model. Three patients are recorded with scalp electroencephalography (sEEG) and three with intracranial electroencephalography (iEEG). A sensitivity of 78-100% and a detection latency of 5-18s has been achieved, while holding the false detection at 0.16-5.31/h. Our results show the potential of Matching Pursuit as a feature extractor for detection of epileptic seizures.

KW - Adult

KW - Algorithms

KW - Artificial Intelligence

KW - Case-Control Studies

KW - Child

KW - Diagnosis, Computer-Assisted

KW - Electroencephalography

KW - Epilepsy

KW - Female

KW - Humans

KW - Male

KW - Middle Aged

KW - Pattern Recognition, Automated

KW - Reproducibility of Results

KW - Sensitivity and Specificity

KW - Young Adult

U2 - 10.1109/iembs.2010.5627265

DO - 10.1109/iembs.2010.5627265

M3 - Journal article

C2 - 21096613

SN - 2375-7477

VL - 2010

SP - 3277

EP - 3280

JO - I E E E Engineering in Medicine and Biology Society. Conference Proceedings

JF - I E E E Engineering in Medicine and Biology Society. Conference Proceedings

T2 - Annual International Conference of the IEEE 2010

Y2 - 31 August 2010 through 4 September 2010

ER -